Hui Chen

4.2k citations
227 papers · 2.6k · h-index 27

Impact in

Papers in

Hui Chen

209 papers receiving 2.5k citations

Peers

Hui Chen
Comparison fields: 5 of 174
  • Health Informatics 30
  • Infectious Diseases 401
  • Virology 74
  • Radiology, Nuclear Medicine and Imaging 342
  • Cardiology and Cardiovascular Medicine 234
Replace Hongyu Miao with:
Hongyu Miao United States
Chih‐Hung Wang Taiwan
René Spijker Netherlands
Ahmed Negida Egypt
Abdus Sattar United States
Qiang Shu China
Éric Renard France
Adi L. Tarca United States
C. C. Tchoyoson Lim Singapore
Justin M. O’Sullivan New Zealand
Hui Chen relative to Hongyu Miao United States Hongyu Miao's profile →
Citations per field
00.5×
Hongyu Miao · 1×
Citations per year

Countries citing papers authored by Hui Chen

Since Specialization
Citations

This map shows the geographic impact of Hui Chen's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Hui Chen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hui Chen more than expected).

Fields of papers citing papers by Hui Chen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Hui Chen. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Hui Chen. The network helps show where Hui Chen may publish in the future.

Co-authors

The 25 scholars most cited alongside Hui Chen, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Hui Chen Line = papers co-authored together Hui Chen links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 227 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2020140
2 201458
3 202053
4 201252
5 201851
6 201850
7 201850
8 202149
9 202046
10 201946
11 201444
12 201944
13 201042
14 201339
15 202036
16 202235
17 201835
18 201334
19 201334
20 201333

About Hui Chen

Hui Chen is a scholar working on Artificial Intelligence, Infectious Diseases, Surgery, Cardiology and Cardiovascular Medicine and Pulmonary and Respiratory Medicine, having authored 227 papers that have together received 2.6k indexed citations. Recurring topics across this work include Vascular Malformations and Hemangiomas (16 papers), Spectroscopy and Chemometric Analyses (11 papers), Machine Learning in Healthcare (11 papers), HIV Research and Treatment (11 papers), HIV/AIDS Research and Interventions (9 papers), Advanced Vision and Imaging (7 papers), Tuberculosis Research and Epidemiology (6 papers) and Epilepsy research and treatment (6 papers). The work is most often cited by research in Health Informatics (30 citations), Infectious Diseases (401 citations), Virology (74 citations), Radiology, Nuclear Medicine and Imaging (342 citations) and Cardiology and Cardiovascular Medicine (234 citations). Hui Chen has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Chao Tan, Zan Lin, Yan Xu, Xiaojie Huang, Gang Ma, Kuan Zhang, Xiaoxi Lin, Ni Wang, Yunbo Jin and Shuo Yan. Their work appears in journals such as Scientific Reports, BMC Infectious Diseases, Medicine, BMC Cardiovascular Disorders and Journal of Medical Internet Research.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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